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Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/9664
Title: Monte Carlo Tree Search Experiments in Hearthstone
Authors: Santos, André
Santos, Pedro A.
Melo, Francisco S.
Keywords: Monte Carlo Tree Search
Artificial intelligence for games
Hearthstone
Issue Date: 2017
Publisher: IEEE
Citation: André Santos, Pedro A. Santos, Francisco S. Melo: “Monte Carlo Tree Search Experiments in Hearthstone”, CiG 2017, New York, USA, IEEE Computer Society, 2017
Abstract: In this paper, we introduce a Monte-Carlo tree search (MCTS) approach for the game “Hearthstone: Heroes of Warcraft”. We argue that, in light of the challenges posed by the game (such as uncertainty and hidden information), Monte Carlo tree search offers an appealing alternative to existing AI players. Additionally, by enriching MCTS with a properly constructed heuristic, it is possible to introduce significant gains in performance.We illustrate through extensive empirical validation the superior performance of our approach against vanilla MCTS and the current state-of-the art AI for Hearthstone.
URI: http://hdl.handle.net/1820/9664
Appears in Collections:1. RAGE Publications

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